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Agro-environmental Monitoring Networks

According to Directive 2001/18/EC, environmental monitoring of GMOs has to be conducted in two different ways:

  • case-specific monitoring (CSM), to confirm that any assumption regarding the occurrence and impact of potential adverse effects of the GMO or its use in the environmental risk assessment are correct;
  • general surveillance (GS), that is used to identify to identify the occurrence of adverse effects of the GMO or its use on human health or the environment which were not anticipated in the environmental risk assessment.

An important component of general surveillance should be the link to environmental monitoring activities already existing in the Member States, as established by the EFSA ultimate guidance of 2011.

Regarding this matter, often the applicants establish monitoring plans generally relying to a large extent to information collected by farmer questionnaires. A reference to existing monitoring networks is often included in such general GS plans, but without any detailed specification or reference to these networks. To fill these gaps, the EU in 2004 established a Working Group to identify modality, data and information needed, as well as the standards to be taken into consideration during the planning of monitoring, and at the same time asked Member States to carry out a survey of the monitoring networks useful for the control of risks associated with the deliberate release of GMOs within their territories. Italy still has not responded to this request. Actions A2 and C2 of Life+ Project MAN-GMP-ITA represent an attempt to answer to the requirements of the EU.

The action has developed along two parallel paths: the first one dedicated to the study of monitoring practices suitable for case-specific monitoring, and the second one devoted to the analysis of the monitoring networks identified.

The results are:

  1. case-specific monitoring: our two case studies were the cultivation of insect resistant maize and herbicide-tolerant oilseed rape. The risk scenarios have focused respectively on potential adverse effects on non-target organisms (for maize) and on the emergence of weeds populations due to gene flow (for oilseed rape), in the agricultural fields and nearby natural areas. For insect-resistant maize were identified as non-target organisms moths and beetles: the  resistance derives from the expression of a protein (the Cry class proteins) that has insecticidal effects against some specific crops pests (eg., the butterfly borer). The Cry proteins are different and have different effects on different target insects. Studies of scientific literature have shown, however, that effects may occur also in other species of non-target insects, in the Lepidoptera order and in the Coccinellidae family among others. For this reason, but also because these organisms are involved in the trophic chains of corn, and sampling methods are simple and economical, we selected butterflies and beetles as bioindicators of potential impacts on non-target organisms. Sampling was carried out in 3 consecutive seasons (2010, 2011 and 2012), from March to September, every two weeks, intensified in the local heyday of the corn (usually in August). We have done particular attention to two species of butterfly, Vanessa atalanta andInachis io, used as bioindicators because they are very sensitive to potential impacts. These species use nettles as nurse plant, so we focused our attention on the presence of this plant in the heyday, and on the presence of the larvae of the two species on these plants. From our field-studies, we can say that butterflies and ladybugs can be used as indicators of impacts on natural and agricultural environment, by sampling and subsequent estimate of populations and the presence/absence of some particularly sensible species. In relation to this last point, we must pay special attention to sensitive species selected. The two we have chosen,Vanessa atalanta and Inachis io are fine as bioindicators in northern Italy areas, where there is abundant presence of nettle in the heyday of the corn; instead, they cannot be used as bioindicators in the central and southern Italy areas, because the nettle in August is mostly dry. In these cases, for example, being more prevalent thistle in the field edge, it can be assumed the use of another species as an indicator, theVanessa cardui. In the second case-study, we had to select and then to perform a field-test of a monitoring activity suitable to verify the occurrence of potential effects related to herbicide oilseed rape. In this case, the potential that pollen of cultivated oilseed rape can cross-pollinate variety of wild crucifers was studied, thus determining a gene flow. This impact is particularly important if GM oilseed rape resistant to herbicides transfers this traits to the wild crucifers, creating so called “super-weeds”. The most appropriate monitoring activity in this case is the wild relatives control in the agricultural fields, especially species related to oilseed rape, through floristic surveys in correspondence of the flowering period, associated with the recording of the main meteorological parameters (minimum, maximum and average temperature, relative humidity, wind speed and direction, precipitation). From the floristic surveys, carried out at the edges of the field and however at distances potentially achievable from the pollen of oilseed rape (more or less 100 m), we observed that such monitoring activity can be conducted within the normal controls required in agriculture (plant health controls), provided that the staff is properly trained on the wild relatives species, such as those potentially interfertile in the concerned area. This monitoring activity is relatively simple and economic, as it is includable in activities already and normally carried out. In the event of more specific researches, such as the quantification of gene flow of oilseed rape, the monitoring activity is more complex and costly, as it requires specific analysis techniques such as molecular analysis.
  2. general surveillance: our purpose was to identify existing agro-environmental networks in Italy (see Tab 1), and then to evaluate and verify their applicability, as such or with modifications, to the monitoring of GMOs. The minimum criteria that these networks need for the applicability to GMOs, may be recognised as follows: the existing activities cover environmental compartments relevant to GMOs, or can be extended to include the areas of interest; the existing activities cover the relevant parameters for GMOs, or can be extended to cover the set of parameters/indicators; the sampling of existing monitoring programs can be combined with those for GMOs, in order to reduce costs; the existing activities allow long-term environmental monitoring and take place at time intervals relevant to GMOs; the monitoring activities are conducted on the basis of a reference standard with standardized protocols, so that the data collected are consistent and uniform. Through the comparison between the characteristics of the identified networks and the methods listed above, we can conclude that: 4 networks are suitable suc as; 7 are suitable after specific modifications; 2 are not suitable; the last 2 are still in the process of activation for which it's not possible at the moment to give an opinion. For further information see the links below.

 

Tab 1. Environmental monitoring networks in Italy

Water quality LTER (Long Term Ecosystem Research Network)
Air quality MITO 2000 (Italian Ornithological Monitoring)
Pollnet National Biodiversity Observatories Network
RAN (National Agrometeorological Network) National Biodiversity Network
BeeNet Phytosanitary Service in Agriculture
CONECOFOR (Forest Ecosystems Controls) IPHEN (Italian Phenological Network)
GLORIA (Global Observation Research Initiative In Alpine Environments) National Network of monitoring of biodiversity and degradation of the soil
Ringing Network

 

Further information:

Applicability summary final tab

Applicability detailed final tab

Summary of final evaluation

Final database

Detailed tabs of the networks

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